{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集形状: (60000, 28, 28)\n",
      "训练集标签数量: 60000\n",
      "测试集形状: (10000, 28, 28)\n",
      "测试集标签数量: 10000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fashion MNIST数据集包含10个类别的服装图片:\n",
      "0: T-shirt/top\n",
      "1: 裤子\n",
      "2: 套头衫\n",
      "3: 连衣裙\n",
      "4: 外套\n",
      "5: 凉鞋\n",
      "6: 衬衫\n",
      "7: 运动鞋\n",
      "8: 包\n",
      "9: 短靴\n",
      "\n",
      "每张图片是28x28像素的灰度图\n",
      "训练集包含60,000张图片，测试集包含10,000张图片\n",
      "像素值范围为0-255\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "\n",
    "# 加载Fashion MNIST数据集\n",
    "train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True,transform=torchvision.transforms.ToTensor())\n",
    "test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True,transform=torchvision.transforms.ToTensor())\n",
    "\n",
    "train_images = train_dataset.data.numpy()\n",
    "train_labels = train_dataset.targets.numpy()\n",
    "test_images = test_dataset.data.numpy() \n",
    "test_labels = test_dataset.targets.numpy()\n",
    "\n",
    "# 定义类别名称\n",
    "class_names = ['T-shirt/top', '裤子', '套头衫', '连衣裙', '外套',\n",
    "               '凉鞋', '衬衫', '运动鞋', '包', '短靴']\n",
    "\n",
    "# 打印数据集基本信息\n",
    "print(\"训练集形状:\", train_images.shape)\n",
    "print(\"训练集标签数量:\", len(train_labels))\n",
    "print(\"测试集形状:\", test_images.shape)\n",
    "print(\"测试集标签数量:\", len(test_labels))\n",
    "\n",
    "# 显示第一张图片\n",
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()\n",
    "\n",
    "# 显示前25张训练图片\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()\n",
    "\n",
    "# 数据集说明\n",
    "print(\"\\nFashion MNIST数据集包含10个类别的服装图片:\")\n",
    "for i, name in enumerate(class_names):\n",
    "    print(f\"{i}: {name}\")\n",
    "print(\"\\n每张图片是28x28像素的灰度图\")\n",
    "print(\"训练集包含60,000张图片，测试集包含10,000张图片\") \n",
    "print(\"像素值范围为0-255\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_dataset[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
      "    0   1   4   0   0   0   0   1   1   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
      "   54   0   0   0   1   3   4   0   0   3]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
      "  144 123  23   0   0   0   0  12  10   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
      "  107 156 161 109  64  23  77 130  72  15]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
      "  216 163 127 121 122 146 141  88 172  66]\n",
      " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
      "  223 223 215 213 164 127 123 196 229   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
      "  235 227 224 222 224 221 223 245 173   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
      "  180 212 210 211 213 223 220 243 202   0]\n",
      " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
      "  169 227 208 218 224 212 226 197 209  52]\n",
      " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
      "  198 221 215 213 222 220 245 119 167  56]\n",
      " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
      "  232 213 218 223 234 217 217 209  92   0]\n",
      " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
      "  222 221 216 223 229 215 218 255  77   0]\n",
      " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
      "  211 218 224 223 219 215 224 244 159   0]\n",
      " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
      "  224 234 176 188 250 248 233 238 215   0]\n",
      " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
      "  255 255 221 234 221 211 220 232 246   0]\n",
      " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
      "  188 154 191 210 204 209 222 228 225   0]\n",
      " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
      "  168 219 221 215 217 223 223 224 229  29]\n",
      " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
      "  239 223 218 212 209 222 220 221 230  67]\n",
      " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
      "  199 206 186 181 177 172 181 205 206 115]\n",
      " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
      "  195 191 198 192 176 156 167 177 210  92]\n",
      " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
      "  210 210 211 188 188 194 192 216 170   0]\n",
      " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
      "  182 182 181 176 166 168  99  58   0   0]\n",
      " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(train_images[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x900 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "for i in range(15):\n",
    "    plt.subplot(3,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x900 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "for i in range(15):\n",
    "    plt.subplot(3,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_dataset[i][0].squeeze(), cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_dataset[i][1]])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集均值: 0.2860\n",
      "数据集标准差: 0.3530\n"
     ]
    }
   ],
   "source": [
    "# 计算训练集的均值和标准差\n",
    "train_data = torch.stack([img for img, _ in train_dataset])\n",
    "mean = train_data.mean()\n",
    "std = train_data.std()\n",
    "\n",
    "print(f'数据集均值: {mean:.4f}')\n",
    "print(f'数据集标准差: {std:.4f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms, datasets\n",
    "\n",
    "# 定义标准化变换\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((mean,), (std,))\n",
    "])\n",
    "\n",
    "# 重新创建数据集,应用标准化\n",
    "train_dataset = datasets.FashionMNIST(\n",
    "    root='data',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "test_dataset = datasets.FashionMNIST(\n",
    "    root='data', \n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 把数据集划分训练集55000和验证集5000，并给DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: 55000\n",
      "验证集大小: 5000\n",
      "测试集大小: 10000\n"
     ]
    }
   ],
   "source": [
    "# 划分训练集和验证集\n",
    "train_size = 55000\n",
    "val_size = 5000\n",
    "train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size],generator=torch.Generator().manual_seed(42))\n",
    "\n",
    "# 创建DataLoader\n",
    "batch_size = 64\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "#shuffle=true表示打乱数据集\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size)\n",
    "\n",
    "print(f'训练集大小: {len(train_dataset)}')\n",
    "print(f'验证集大小: {len(val_dataset)}')\n",
    "print(f'测试集大小: {len(test_dataset)}')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "每个批次的大小: 64\n",
      "训练批次数: 860\n"
     ]
    }
   ],
   "source": [
    "# 打印批次大小和训练批次数\n",
    "print(f'每个批次的大小: {batch_size}')\n",
    "print(f'训练批次数: {len(train_loader)}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 搭建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NeuralNetwork(\n",
      "  (fc1): Linear(in_features=784, out_features=300, bias=True)\n",
      "  (fc2): Linear(in_features=300, out_features=100, bias=True)\n",
      "  (fc3): Linear(in_features=100, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "# 定义神经网络模型\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        # 输入层到第一个隐藏层 (28*28 -> 300)\n",
    "        self.fc1 = nn.Linear(28*28, 300)\n",
    "        # 第一个隐藏层到第二个隐藏层 (300 -> 100)\n",
    "        self.fc2 = nn.Linear(300, 100)\n",
    "        # 第二个隐藏层到输出层 (100 -> 10)\n",
    "        self.fc3 = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        #print('输入形状:', x.shape)\n",
    "        # 将输入展平为一维向量\n",
    "        x = x.view(-1, 28*28)\n",
    "        #print('展平后形状:', x.shape)\n",
    "        # 第一个隐藏层使用ReLU激活函数\n",
    "        x = F.relu(self.fc1(x))\n",
    "        #print('第一个隐藏层输出形状:', x.shape)\n",
    "        # 第二个隐藏层使用ReLU激活函数\n",
    "        x = F.relu(self.fc2(x))\n",
    "        #print('第二个隐藏层输出形状:', x.shape)\n",
    "        # 输出层不使用激活函数\n",
    "        x = self.fc3(x)\n",
    "        #print('输出层形状:', x.shape)\n",
    "        return x\n",
    "\n",
    "# 创建模型实例\n",
    "model = NeuralNetwork()\n",
    "print(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "输入数据形状: torch.Size([64, 1, 28, 28])\n",
      "标签形状: torch.Size([64])\n",
      "\n",
      "模型输出形状: torch.Size([64, 10])\n",
      "预测结果: tensor([7, 1, 6, 1, 6, 1, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 6, 0, 3, 6, 6, 0, 7,\n",
      "        7, 0, 6, 0, 6, 7, 0, 3, 0, 0, 3, 7, 3, 0, 0, 3, 0, 3, 0, 0, 6, 7, 6, 0,\n",
      "        6, 7, 1, 6, 3, 6, 0, 0, 0, 0, 6, 0, 6, 6, 6, 0])\n",
      "真实标签: tensor([7, 1, 1, 5, 1, 9, 6, 3, 2, 5, 4, 5, 6, 4, 7, 5, 7, 8, 5, 2, 6, 0, 2, 1,\n",
      "        7, 5, 3, 6, 4, 1, 5, 4, 2, 2, 8, 7, 6, 0, 1, 0, 4, 0, 9, 6, 0, 5, 3, 4,\n",
      "        0, 9, 8, 1, 0, 0, 4, 3, 5, 4, 1, 6, 1, 0, 3, 2])\n"
     ]
    }
   ],
   "source": [
    "# 获取第一个批次的数据\n",
    "images, labels = next(iter(train_loader))\n",
    "\n",
    "# 打印输入数据的形状\n",
    "print('输入数据形状:', images.shape)\n",
    "print('标签形状:', labels.shape)\n",
    "\n",
    "# 前向传播\n",
    "outputs = model(images)\n",
    "\n",
    "# 打印输出结果\n",
    "print('\\n模型输出形状:', outputs.shape)\n",
    "print('预测结果:', torch.argmax(outputs, dim=1))\n",
    "print('真实标签:', labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型总参数量: 266610\n",
      "fc1.weight 的参数量: 235200\n",
      "fc1.bias 的参数量: 300\n",
      "fc2.weight 的参数量: 30000\n",
      "fc2.bias 的参数量: 100\n",
      "fc3.weight 的参数量: 1000\n",
      "fc3.bias 的参数量: 10\n"
     ]
    }
   ],
   "source": [
    "# 计算模型总参数量\n",
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "print('模型总参数量:', total_params)\n",
    "\n",
    "# 打印每一层的参数量\n",
    "for name, param in model.named_parameters():\n",
    "    print(f'{name} 的参数量: {param.numel()}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 早停"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EarlyStopping:\n",
    "    \"\"\"早停类,用于防止过拟合\n",
    "    \n",
    "    该类实现了早停机制,通过监控验证集准确率来判断是否需要提前终止训练,以防止模型过拟合。\n",
    "    当验证集准确率在连续多轮训练中没有得到改善时,即可认为模型已经达到最佳状态,应当停止训练。\n",
    "    \"\"\"\n",
    "    def __init__(self, patience=7, verbose=False, delta=0.0):\n",
    "        \"\"\"初始化早停类的相关参数\n",
    "        \n",
    "        Args:\n",
    "            patience (int): 容忍早停的轮数,即经过多少轮验证集准确率都没有改善才停止训练\n",
    "            verbose (bool): 是否打印早停信息,True表示打印,False表示不打印\n",
    "            delta (float): 最小改善阈值,小于该阈值的改善被视为无效改善\n",
    "        \"\"\"\n",
    "        self.patience = patience  # 容忍轮数\n",
    "        self.verbose = verbose   # 是否打印信息标志\n",
    "        self.counter = 0         # 计数器,记录连续未改善的轮数\n",
    "        self.best_acc = None     # 记录最佳准确率\n",
    "        self.early_stop = False  # 是否早停的标志\n",
    "        self.val_acc_max = float('-inf')  # 记录最大验证准确率\n",
    "        self.delta = delta       # 改善阈值\n",
    "\n",
    "    def __call__(self, val_acc):\n",
    "        \"\"\"每个epoch结束时调用该方法来更新早停状态\n",
    "        \n",
    "        Args:\n",
    "            val_acc (float): 当前epoch的验证集准确率\n",
    "            \n",
    "        当验证集准确率连续patience轮未改善时,early_stop标志会被置为True\n",
    "        \"\"\"\n",
    "        # 首次调用时记录准确率\n",
    "        if self.best_acc is None:\n",
    "            self.best_acc = val_acc\n",
    "        # 当前准确率未超过最佳准确率+阈值\n",
    "        elif val_acc < self.best_acc + self.delta:\n",
    "            self.counter += 1  # 计数器加1\n",
    "            if self.verbose:  # 如果启用详细输出\n",
    "                print(f'早停计数器: {self.counter} / {self.patience}')\n",
    "            if self.counter >= self.patience:  # 判断是否达到容忍轮数\n",
    "                self.early_stop = True  # 设置早停标志\n",
    "                print(f'早停! 验证集准确率没有改善,连续{self.counter}轮未改善，最佳准确率为{self.best_acc:.2f}%')\n",
    "        else:\n",
    "            self.best_acc = val_acc  # 更新最佳准确率\n",
    "            self.counter = 0  # 重置计数器\n",
    "\n",
    "        return self.early_stop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "class ModelCheckpoint:\n",
    "    \"\"\"\n",
    "    模型检查点类,用于保存模型权重\n",
    "    \n",
    "    可以选择只保存最佳模型,或者保存每个epoch后的模型权重\n",
    "    \"\"\"\n",
    "    def __init__(self, save_best_only=True, save_path='./checkpoint', verbose=True):\n",
    "        \"\"\"初始化模型检查点类\n",
    "        \n",
    "        Args:\n",
    "            save_best_only (bool): 是否只保存最佳模型,True表示只保存最佳模型,False表示每个epoch都保存\n",
    "            save_path (str): 模型保存路径\n",
    "            verbose (bool): 是否打印保存信息\n",
    "        \"\"\"\n",
    "        self.save_best_only = save_best_only\n",
    "        self.save_path = save_path\n",
    "        self.verbose = verbose\n",
    "        self.best_acc = float('-inf')\n",
    "        if not os.path.exists(save_path):\n",
    "            os.makedirs(save_path)\n",
    "        \n",
    "    def __call__(self, model, val_acc, epoch):\n",
    "        \"\"\"每个epoch结束时调用该方法来保存模型\n",
    "        \n",
    "        Args:\n",
    "            model: 需要保存的模型\n",
    "            val_acc (float): 当前epoch的验证集准确率\n",
    "            epoch (int): 当前的训练轮数\n",
    "        \"\"\"\n",
    "        filename = f'/epoch_{epoch+1}.pth'\n",
    "        save_path = os.path.join(self.save_path, f'best_model.pth')\n",
    "        if self.save_best_only:\n",
    "            if val_acc > self.best_acc:\n",
    "                self.best_acc = val_acc\n",
    "                if os.path.exists(save_path):\n",
    "                    os.remove(save_path)\n",
    "                torch.save(model.state_dict(), save_path)\n",
    "                if self.verbose:\n",
    "                    print(f'模型在第{epoch+1}轮得到改善,准确率提升至{val_acc:.2f}%,已保存模型')\n",
    "                \n",
    "                \n",
    "        else:\n",
    "            # 如果保存所有epoch的模型,则使用epoch编号来区分不同文件\n",
    "            save_path = os.path.join(self.save_path, f'epoch_{epoch+1}.pth')\n",
    "            torch.save(model.state_dict(), save_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./checkpoint\\epoch_5.pth\n"
     ]
    }
   ],
   "source": [
    "save_path_1 = './checkpoint'\n",
    "filename = 'epoch_5.pth'\n",
    "save_path = os.path.join(save_path_1, filename)\n",
    "print(save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch_5.pth\n",
      "checkpoint\\epoch_5.pth\n"
     ]
    }
   ],
   "source": [
    "save_path='checkpoint'\n",
    "filename = f'epoch_{5}.pth'\n",
    "print(filename)\n",
    "save_path = os.path.join(save_path, filename)\n",
    "print(save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 设置交叉熵损失函数，SGD优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "损失函数: CrossEntropyLoss()\n",
      "优化器: SGD (\n",
      "Parameter Group 0\n",
      "    dampening: 0\n",
      "    differentiable: False\n",
      "    foreach: None\n",
      "    fused: None\n",
      "    lr: 0.01\n",
      "    maximize: False\n",
      "    momentum: 0.9\n",
      "    nesterov: False\n",
      "    weight_decay: 0\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import torch.optim as optim\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()#交叉熵损失函数\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)#lr是学习率，momentum是动量\n",
    "\n",
    "print('损失函数:', criterion)\n",
    "print('优化器:', optimizer)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=12, micro=3, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.3\n",
      "numpy 2.1.3\n",
      "pandas 2.3.0\n",
      "sklearn 1.7.0\n",
      "torch 2.7.1+cu126\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import torch\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编写评估函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, test_loader, compute_loss=False):\n",
    "    \"\"\"\n",
    "    评估模型在测试集上的准确率和损失\n",
    "    \n",
    "    参数:\n",
    "        model: 神经网络模型\n",
    "        test_loader: 测试数据加载器\n",
    "        compute_loss: 是否计算损失,默认为False\n",
    "    \n",
    "    返回:\n",
    "        accuracy: 准确率\n",
    "        test_loss: 如果compute_loss为True则返回测试损失,否则返回None\n",
    "    \"\"\"\n",
    "    model.eval()  # 设置为评估模式\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    test_loss = 0\n",
    "    \n",
    "    if compute_loss:\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "    \n",
    "    with torch.no_grad():  # 不计算梯度\n",
    "        for images, labels in test_loader:\n",
    "            images,labels=images.to(device),labels.to(device)# 将数据移动到GPU\n",
    "            outputs = model(images)\n",
    "            if compute_loss:\n",
    "                loss = criterion(outputs, labels)\n",
    "                test_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1) # 返回两个值，第一个是最大值，第二个是最大值的索引\n",
    "            total += labels.size(0) # 标签的个数\n",
    "            correct += (predicted == labels).sum().item()# 预测正确的个数\n",
    "    \n",
    "    accuracy = 100 * correct / total\n",
    "    #print(f'准确率: {accuracy:.2f}%')\n",
    "    \n",
    "    if compute_loss:\n",
    "        test_loss = test_loss / len(test_loader)\n",
    "        #print(f'损失: {test_loss:.4f}')\n",
    "        return accuracy, test_loss\n",
    "    return accuracy, None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编写训练函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs, early_stopping=None, model_checkpoint=None):\n",
    "    \"\"\"\n",
    "    训练神经网络模型\n",
    "    \n",
    "    参数:\n",
    "        model: 神经网络模型\n",
    "        train_loader: 训练数据加载器\n",
    "        val_loader: 验证数据加载器\n",
    "        criterion: 损失函数\n",
    "        optimizer: 优化器\n",
    "        num_epochs: 训练轮数\n",
    "        early_stopping: 早停对象\n",
    "        model_checkpoint: 模型检查点对象\n",
    "    \n",
    "    返回:\n",
    "        model: 训练好的模型\n",
    "        history: 训练历史记录,包含每个epoch的训练和验证指标\n",
    "    \"\"\"\n",
    "    # 将模型移动到GPU\n",
    "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model = model.to(device)\n",
    "    \n",
    "    model.train()  # 设置为训练模式\n",
    "    history = {\n",
    "        'train_loss': [],\n",
    "        'train_acc': [],\n",
    "        'val_loss': [],\n",
    "        'val_acc': []\n",
    "    }\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        running_loss = 0.0\n",
    "        for i, (images, labels) in enumerate(train_loader):\n",
    "            images, labels = images.to(device), labels.to(device)  # 将数据移动到GPU\n",
    "            \n",
    "            optimizer.zero_grad()  # 清除之前的梯度\n",
    "            # 前向传播\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            # 反向传播和优化\n",
    "            loss.backward()  # 反向传播\n",
    "            optimizer.step()  # 更新参数\n",
    "            \n",
    "            running_loss += loss.item()\n",
    "            \n",
    "            # 每100个批次打印一次训练状态\n",
    "            if (i + 1) % 100 == 0:\n",
    "                # print(f'轮次 [{epoch+1}/{num_epochs}], 批次 [{i+1}/{len(train_loader)}], '\n",
    "                #       f'损失: {running_loss/100:.4f}')\n",
    "                running_loss = 0.0\n",
    "                    \n",
    "        # 在每个epoch结束后评估模型\n",
    "        print(f'\\nEpoch {epoch+1} 完成')\n",
    "        \n",
    "        # 计算训练集准确率和损失\n",
    "        train_acc, train_loss = evaluate(model, train_loader, compute_loss=True)\n",
    "        print(f'训练集准确率:{train_acc:.2f}%,训练集损失：{train_loss:.4f}')\n",
    "        history['train_acc'].append(train_acc)\n",
    "        history['train_loss'].append(train_loss)\n",
    "        \n",
    "        # 计算验证集准确率和损失\n",
    "        val_acc, val_loss = evaluate(model, val_loader, compute_loss=True)\n",
    "        print(f'验证集准确率：{val_acc:.2f}%,验证集损失：{val_loss:.4f}')\n",
    "        history['val_acc'].append(val_acc)\n",
    "        history['val_loss'].append(val_loss)\n",
    "        \n",
    "        # 保存最佳模型\n",
    "        if model_checkpoint is not None:\n",
    "            model_checkpoint(model, val_acc, epoch)\n",
    "        \n",
    "        # 早停检查\n",
    "        if early_stopping is not None:\n",
    "            if early_stopping(val_acc):\n",
    "                print(f'触发早停! 在epoch {epoch+1}')\n",
    "            # 加载最佳模型\n",
    "            #model.load_state_dict(torch.load(model_checkpoint.path))\n",
    "                break\n",
    "            \n",
    "        model.train()  # 切回训练模式\n",
    "            \n",
    "        \n",
    "    print('训练完成!')\n",
    "    return model, history\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 带有tqdm的训练函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm\n",
    "def train_model_1(model, train_loader, val_loader, criterion, optimizer, num_epochs, early_stopping=None, model_checkpoint=None):\n",
    "    \"\"\"\n",
    "    训练神经网络模型\n",
    "    \n",
    "    参数:\n",
    "        model: 神经网络模型\n",
    "        train_loader: 训练数据加载器\n",
    "        val_loader: 验证数据加载器\n",
    "        criterion: 损失函数\n",
    "        optimizer: 优化器\n",
    "        num_epochs: 训练轮数\n",
    "        early_stopping: 早停对象\n",
    "        model_checkpoint: 模型检查点对象\n",
    "    \n",
    "    返回:\n",
    "        model: 训练好的模型\n",
    "        history: 训练历史记录,包含每个epoch的训练和验证指标\n",
    "    \"\"\"\n",
    "    # 将模型移动到GPU\n",
    "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model = model.to(device)\n",
    "    \n",
    "    model.train()  # 设置为训练模式\n",
    "    history = {\n",
    "        'train': [],\n",
    "        'val': []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    with tqdm(total=num_epochs*len(train_loader)) as pbar:\n",
    "        for epoch in range(num_epochs):\n",
    "            \n",
    "            for i, (images, labels) in enumerate(train_loader):\n",
    "                images, labels = images.to(device), labels.to(device)  # 将数据移动到GPU\n",
    "                \n",
    "                optimizer.zero_grad()  # 清除之前的梯度\n",
    "                # 前向传播\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                \n",
    "                # 反向传播和优化\n",
    "                loss.backward()  # 反向传播\n",
    "                optimizer.step()  # 更新参数\n",
    "\n",
    "\n",
    "                #计算准确率\n",
    "                preds = outputs.argmax(axis=-1)\n",
    "                acc = (preds == labels).float().mean().item()*100\n",
    "                loss_value = loss.cpu().item()\n",
    "\n",
    "                #记录训练数据\n",
    "                history['train'].append({\n",
    "                    \"loss\":loss_value,\n",
    "                    \"acc\":acc,\n",
    "                    \"step\":global_step\n",
    "                })\n",
    "                \n",
    "                \n",
    "                # 评估\n",
    "                if global_step  % 100 == 0:\n",
    "                    val_acc, val_loss = evaluate(model, val_loader, compute_loss=True)\n",
    "                    history['val'].append({\n",
    "                        \"loss\":val_loss,\n",
    "                        \"acc\":val_acc,\n",
    "                        \"step\":global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "                    # 保存最佳模型\n",
    "                    if model_checkpoint is not None:\n",
    "                        model_checkpoint(model, val_acc, epoch)\n",
    "            \n",
    "                    # 早停检查\n",
    "                    if early_stopping is not None:\n",
    "                        if early_stopping(val_acc):\n",
    "                            print(f'触发早停! 在epoch {epoch+1}')\n",
    "                        # 加载最佳模型\n",
    "                        #model.load_state_dict(torch.load(model_checkpoint.path))\n",
    "                            print('训练完成!')\n",
    "                            return model,history\n",
    "                    \n",
    "                \n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\":epoch+1,\"loss\":f\"{loss_value:.4f}\",\"acc\":f\"{acc:.2f}%\"})\n",
    "            \n",
    "        \n",
    "    print('训练完成!')\n",
    "    return model, history\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda:0 device\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44b6af8894214956b4fe74db7b4b318b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/17200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型在第1轮得到改善,准确率提升至12.74%,已保存模型\n",
      "模型在第1轮得到改善,准确率提升至68.12%,已保存模型\n",
      "模型在第1轮得到改善,准确率提升至79.56%,已保存模型\n",
      "模型在第1轮得到改善,准确率提升至82.12%,已保存模型\n",
      "模型在第1轮得到改善,准确率提升至83.54%,已保存模型\n",
      "模型在第1轮得到改善,准确率提升至83.58%,已保存模型\n",
      "模型在第1轮得到改善,准确率提升至84.56%,已保存模型\n",
      "模型在第1轮得到改善,准确率提升至84.94%,已保存模型\n",
      "模型在第2轮得到改善,准确率提升至85.32%,已保存模型\n",
      "模型在第2轮得到改善,准确率提升至85.74%,已保存模型\n",
      "模型在第2轮得到改善,准确率提升至86.12%,已保存模型\n",
      "模型在第2轮得到改善,准确率提升至86.36%,已保存模型\n",
      "模型在第2轮得到改善,准确率提升至86.88%,已保存模型\n",
      "模型在第3轮得到改善,准确率提升至87.00%,已保存模型\n",
      "模型在第3轮得到改善,准确率提升至87.24%,已保存模型\n",
      "模型在第3轮得到改善,准确率提升至87.36%,已保存模型\n",
      "早停! 验证集准确率没有改善,连续5轮未改善，最佳准确率为87.36%\n",
      "触发早停! 在epoch 4\n",
      "训练完成!\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(f'Using {device} device')\n",
    "\n",
    "# 实例化早停和模型检查点\n",
    "early_stopping = EarlyStopping(patience=5, delta=0.001)\n",
    "model_checkpoint = ModelCheckpoint(save_best_only=True, save_path='./checkpoint', verbose=True)\n",
    "\n",
    "model,history=train_model_1(model,train_loader,val_loader,criterion,optimizer,num_epochs=20,\n",
    "                         early_stopping=early_stopping,\n",
    "                         model_checkpoint=model_checkpoint)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘制准确率和损失曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves1(history, sample_step=500):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(history[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(history[\"val\"]).set_index(\"step\")\n",
    "    #print(train_df.head())\n",
    "    #print(val_df.head())\n",
    "    # plot\n",
    "    fig_num = len(train_df.columns) #因为有loss和acc两个指标，所以画个子图\n",
    "    fig, axs = plt.subplots(1, fig_num, figsize=(5 * fig_num, 5)) #fig_num个子图，figsize是子图大小\n",
    "    for idx, item in enumerate(train_df.columns):    \n",
    "        #index是步数，item是指标名字\n",
    "        axs[idx].plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        axs[idx].plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        axs[idx].grid()\n",
    "        axs[idx].legend()\n",
    "        x_data=range(0, train_df.index[-1], 5000) #每隔5000步标出一个点\n",
    "        axs[idx].set_xticks(x_data)\n",
    "        axs[idx].set_xticklabels(map(lambda x: f\"{int(x/1000)}k\", x_data)) #map生成labal\n",
    "        axs[idx].set_xlabel(\"step\")\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_learning_curves1(history, sample_step=100)  #横坐标是 steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集准确率: 86.33%,测试集损失: 0.3767\n"
     ]
    }
   ],
   "source": [
    "# 在测试集上评估模型\n",
    "model.eval()  # 设置为评估模式\n",
    "test_acc,test_loss=evaluate(model,test_loader,compute_loss=True)\n",
    "print(f'测试集准确率: {test_acc:.2f}%,测试集损失: {test_loss:.4f}')\n"
   ]
  }
 ],
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